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NVIDIA NemoClaw CKG MCP server

by Yarmoluk·io.github.Yarmoluk/ckg-nvidia-nemoclaw·v0.10.3

NVIDIA NemoClaw knowledge graph — 55 nodes, F1 0.576 (+269% vs RAG), 11x fewer tokens. MCP-native.

B75/100grade B
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B75/100

full report

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107 downloads/wk

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NVIDIA NemoClaw CKG tools (8)

write = sends, deletes, buys or posts
  • ask_nemoclawFree

    Answer a question about NVIDIA NemoClaw by traversing the knowledge graph. Covers: agent runtimes (OpenClaw/Hermes/Deep Agents), OpenShell platform, inference routing, network policy, security layers, deployment paths, progressive tool disclosure, managed MCP servers, snapshots, shields, FOX Blueprint, Nemotron 3 Ultra ecosystem, and platform support. Args: question: Your question about NemoClaw concepts or architecture.

  • get_prerequisitesFree

    Return the full upstream prerequisite chain for a NemoClaw concept. Useful for understanding what a concept depends on end-to-end. Args: concept: Exact or partial concept label.

  • list_domainsFree

    List available domains in this CKG server.

  • query_ckgFree

    Return the typed subgraph around a NemoClaw concept. Args: concept: Exact or partial concept label (e.g. 'OpenClaw', 'NetworkPolicy', 'L7Proxy'). depth: Traversal hops (1–5, default 3).

  • query_intersectFree

    Answer a conjunctive query: concepts reachable from EVERY anchor at once (A AND B). query_ckg walks outward from one concept. This intersects the reachable sets of two or more, which is the shape of most real questions — "the component that satisfies A AND applies to B". Neither anchor alone answers it; the answer lives in the overlap. Every branch is an exact set of declared edges, so the intersection is exact. A concept appears only if a declared path reaches it from each anchor. A relation missing from the graph produces an empty result, never a guess. Args: branches: Two or more branches. Either a bare anchor ("TensorRT-LLM"), which takes everything within `depth` hops, or an anchor plus an explicit relation path using '>' ("TensorRT-LLM > REQUIRES > ENABLES"), where each relation replaces the frontier. '*' matches any relation. Mix both forms freely. depth: Hops for bare-anchor branches, 1-5 (default 2). Ignored for explicit paths. direction: 'out' follows dependencies, 'in' follows them backwards, 'both' (default). mode: 'AND' (default) intersects branches; 'OR' unions them. limit: Max concepts listed, 1-200 (default 40). The true count is always shown. Returns: Markdown with the query plan and its per-step set sizes, then the answer set with taxonomy tags. Reports which branch was empty when the intersection is empty.

  • route_queryFree

    Route a NemoClaw question to the optimal model and reasoning approach via graph depth. The CKG graph IS the router. NemoClaw's dependency chains (e.g. OpenShell → L7Proxy → CorporateCA → mTLS) are deep and typed — hop depth is a deterministic complexity signal. No heuristic: the graph decides which model and reasoning approach to use. Routing table: hop_depth 1 → haiku · direct (single concept lookup) hop_depth 2 → sonnet · generic_cot (moderate chain) hop_depth 3+ → opus · sparql_cot (deep chain, structured reasoning required) Args: question: Concept name or natural language question about NemoClaw / OpenShell. Returns: model_tier + reasoning_approach + why + context subgraph to inject before LLM call.

  • search_conceptsFree

    Fuzzy search for NemoClaw concepts by name or keyword. Args: query: Partial name or keyword (e.g. 'policy', 'inference', 'agent').

  • verify_sourceFree

    Return the authoritative source URL and content hash for a NemoClaw concept node. Every node in the CKG was declared from a specific source document. This tool returns the source URL (where the node came from) and the SHA-256 hash of that document's bytes at extraction time. A hash mismatch on re-fetch means either the source has changed (stale edge — re-extract) or the graph was patched without re-fetching (silent edit — investigate). Audit chain: edge answer → graph commit → source_hash → source_url (fetch hint) Verification: curl -s <source_url> | sha256sum # compare output to source_hash Args: concept: Exact or partial concept label (e.g. 'CorporateCA', 'L7Proxy').

Public scan report

scanner v0.1.9 · 2026-09-24 · same rubric, same numbers if you re-run it

no findings
  • Code scan13 source files scanned25/25
  • Live reliabilityremote reachable in 679ms20/20
  • Tool poisoning8 tool descriptions checked15/15
  • Auth qualityopen endpoint, read-only tools10/15
  • Maintenancerepository not readable: repo not found3/15
  • Maintainer identityno repository or website to verify2/10
Overall 75/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

Install directly

claude mcp add --transport http ckg-nvidia-nemoclaw https://ckg-nvidia-nemoclaw.onrender.com/mcp
Add to Cursor

NVIDIA NemoClaw CKG: common questions

Is NVIDIA NemoClaw CKG MCP server safe?
Mostly: it is graded B (75/100). Read the NVIDIA NemoClaw CKG safety report
How do I install NVIDIA NemoClaw CKG?
It runs remotely at ckg-nvidia-nemoclaw.onrender.com. Add it to Claude Code, Claude Desktop or Cursor with the snippets above, or call it through the mcp.market gateway without installing anything.
Does NVIDIA NemoClaw CKG need an API key?
Not as far as the registry entry and our scan can tell: no credentials are declared or required.
Is NVIDIA NemoClaw CKG maintained?
The latest release is v0.10.3.
Is NVIDIA NemoClaw CKG up?
100% of our last 28 checks got an answer. We check remote servers about four times a day.
What can I use instead of NVIDIA NemoClaw CKG?
Servers from other publishers that do the same job: Google Surf MCP server, Total Agent Memory MCP server and rag-rat MCP server. Compare all NVIDIA NemoClaw CKG alternatives.

Alternatives to NVIDIA NemoClaw CKG

Same job from other publishers: the closest match first, then the best rated.

All NVIDIA NemoClaw CKG alternatives →
  • Google Surf
    Web, academic and code search with graph RAG, data lineage, ontology and cross-project schema links.
    B
  • Total Agent Memory
    Persistent local memory for coding agents: temporal knowledge graph, procedural and episodic recall
    A
  • rag-rat
    Repository intelligence, code graph, history, papertrail, and cross-agent memory for coding agents.
    B
  • Basic Memory
    Local-first knowledge management with bi-directional LLM sync via Markdown files.
    C
  • IWE
    Markdown knowledge base as agent memory. Runs against the notes directory it is started in.
    A

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